{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diabetes Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
       "          0.01990842, -0.01764613],\n",
       "        [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
       "         -0.06832974, -0.09220405],\n",
       "        [ 0.08529891,  0.05068012,  0.04445121, ..., -0.00259226,\n",
       "          0.00286377, -0.02593034],\n",
       "        ...,\n",
       "        [ 0.04170844,  0.05068012, -0.01590626, ..., -0.01107952,\n",
       "         -0.04687948,  0.01549073],\n",
       "        [-0.04547248, -0.04464164,  0.03906215, ...,  0.02655962,\n",
       "          0.04452837, -0.02593034],\n",
       "        [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
       "         -0.00421986,  0.00306441]]),\n",
       " 'target': array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
       "         69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
       "         68., 245., 184., 202., 137.,  85., 131., 283., 129.,  59., 341.,\n",
       "         87.,  65., 102., 265., 276., 252.,  90., 100.,  55.,  61.,  92.,\n",
       "        259.,  53., 190., 142.,  75., 142., 155., 225.,  59., 104., 182.,\n",
       "        128.,  52.,  37., 170., 170.,  61., 144.,  52., 128.,  71., 163.,\n",
       "        150.,  97., 160., 178.,  48., 270., 202., 111.,  85.,  42., 170.,\n",
       "        200., 252., 113., 143.,  51.,  52., 210.,  65., 141.,  55., 134.,\n",
       "         42., 111.,  98., 164.,  48.,  96.,  90., 162., 150., 279.,  92.,\n",
       "         83., 128., 102., 302., 198.,  95.,  53., 134., 144., 232.,  81.,\n",
       "        104.,  59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n",
       "        173., 180.,  84., 121., 161.,  99., 109., 115., 268., 274., 158.,\n",
       "        107.,  83., 103., 272.,  85., 280., 336., 281., 118., 317., 235.,\n",
       "         60., 174., 259., 178., 128.,  96., 126., 288.,  88., 292.,  71.,\n",
       "        197., 186.,  25.,  84.,  96., 195.,  53., 217., 172., 131., 214.,\n",
       "         59.,  70., 220., 268., 152.,  47.,  74., 295., 101., 151., 127.,\n",
       "        237., 225.,  81., 151., 107.,  64., 138., 185., 265., 101., 137.,\n",
       "        143., 141.,  79., 292., 178.,  91., 116.,  86., 122.,  72., 129.,\n",
       "        142.,  90., 158.,  39., 196., 222., 277.,  99., 196., 202., 155.,\n",
       "         77., 191.,  70.,  73.,  49.,  65., 263., 248., 296., 214., 185.,\n",
       "         78.,  93., 252., 150.,  77., 208.,  77., 108., 160.,  53., 220.,\n",
       "        154., 259.,  90., 246., 124.,  67.,  72., 257., 262., 275., 177.,\n",
       "         71.,  47., 187., 125.,  78.,  51., 258., 215., 303., 243.,  91.,\n",
       "        150., 310., 153., 346.,  63.,  89.,  50.,  39., 103., 308., 116.,\n",
       "        145.,  74.,  45., 115., 264.,  87., 202., 127., 182., 241.,  66.,\n",
       "         94., 283.,  64., 102., 200., 265.,  94., 230., 181., 156., 233.,\n",
       "         60., 219.,  80.,  68., 332., 248.,  84., 200.,  55.,  85.,  89.,\n",
       "         31., 129.,  83., 275.,  65., 198., 236., 253., 124.,  44., 172.,\n",
       "        114., 142., 109., 180., 144., 163., 147.,  97., 220., 190., 109.,\n",
       "        191., 122., 230., 242., 248., 249., 192., 131., 237.,  78., 135.,\n",
       "        244., 199., 270., 164.,  72.,  96., 306.,  91., 214.,  95., 216.,\n",
       "        263., 178., 113., 200., 139., 139.,  88., 148.,  88., 243.,  71.,\n",
       "         77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n",
       "         58., 262., 206., 233., 242., 123., 167.,  63., 197.,  71., 168.,\n",
       "        140., 217., 121., 235., 245.,  40.,  52., 104., 132.,  88.,  69.,\n",
       "        219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n",
       "         43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
       "        140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
       "         84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
       "         94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
       "        220.,  57.]),\n",
       " 'frame': None,\n",
       " 'DESCR': '.. _diabetes_dataset:\\n\\nDiabetes dataset\\n----------------\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative measure of disease progression one year after baseline.\\n\\n**Data Set Characteristics:**\\n\\n  :Number of Instances: 442\\n\\n  :Number of Attributes: First 10 columns are numeric predictive values\\n\\n  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\\n\\n  :Attribute Information:\\n      - age     age in years\\n      - sex\\n      - bmi     body mass index\\n      - bp      average blood pressure\\n      - s1      tc, T-Cells (a type of white blood cells)\\n      - s2      ldl, low-density lipoproteins\\n      - s3      hdl, high-density lipoproteins\\n      - s4      tch, thyroid stimulating hormone\\n      - s5      ltg, lamotrigine\\n      - s6      glu, blood sugar level\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttps://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\\n(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)',\n",
       " 'feature_names': ['age',\n",
       "  'sex',\n",
       "  'bmi',\n",
       "  'bp',\n",
       "  's1',\n",
       "  's2',\n",
       "  's3',\n",
       "  's4',\n",
       "  's5',\n",
       "  's6'],\n",
       " 'data_filename': 'C:\\\\Users\\\\win10\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\diabetes_data.csv.gz',\n",
       " 'target_filename': 'C:\\\\Users\\\\win10\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\diabetes_target.csv.gz'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _diabetes_dataset:\n",
      "\n",
      "Diabetes dataset\n",
      "----------------\n",
      "\n",
      "Ten baseline variables, age, sex, body mass index, average blood\n",
      "pressure, and six blood serum measurements were obtained for each of n =\n",
      "442 diabetes patients, as well as the response of interest, a\n",
      "quantitative measure of disease progression one year after baseline.\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "  :Number of Instances: 442\n",
      "\n",
      "  :Number of Attributes: First 10 columns are numeric predictive values\n",
      "\n",
      "  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
      "\n",
      "  :Attribute Information:\n",
      "      - age     age in years\n",
      "      - sex\n",
      "      - bmi     body mass index\n",
      "      - bp      average blood pressure\n",
      "      - s1      tc, T-Cells (a type of white blood cells)\n",
      "      - s2      ldl, low-density lipoproteins\n",
      "      - s3      hdl, high-density lipoproteins\n",
      "      - s4      tch, thyroid stimulating hormone\n",
      "      - s5      ltg, lamotrigine\n",
      "      - s6      glu, blood sugar level\n",
      "\n",
      "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\n",
      "\n",
      "Source URL:\n",
      "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n",
      "\n",
      "For more information see:\n",
      "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n",
      "(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n"
     ]
    }
   ],
   "source": [
    "print(dataset['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
       "          0.01990842, -0.01764613],\n",
       "        [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
       "         -0.06832974, -0.09220405],\n",
       "        [ 0.08529891,  0.05068012,  0.04445121, ..., -0.00259226,\n",
       "          0.00286377, -0.02593034],\n",
       "        ...,\n",
       "        [ 0.04170844,  0.05068012, -0.01590626, ..., -0.01107952,\n",
       "         -0.04687948,  0.01549073],\n",
       "        [-0.04547248, -0.04464164,  0.03906215, ...,  0.02655962,\n",
       "          0.04452837, -0.02593034],\n",
       "        [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
       "         -0.00421986,  0.00306441]]),\n",
       " 'target': array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
       "         69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
       "         68., 245., 184., 202., 137.,  85., 131., 283., 129.,  59., 341.,\n",
       "         87.,  65., 102., 265., 276., 252.,  90., 100.,  55.,  61.,  92.,\n",
       "        259.,  53., 190., 142.,  75., 142., 155., 225.,  59., 104., 182.,\n",
       "        128.,  52.,  37., 170., 170.,  61., 144.,  52., 128.,  71., 163.,\n",
       "        150.,  97., 160., 178.,  48., 270., 202., 111.,  85.,  42., 170.,\n",
       "        200., 252., 113., 143.,  51.,  52., 210.,  65., 141.,  55., 134.,\n",
       "         42., 111.,  98., 164.,  48.,  96.,  90., 162., 150., 279.,  92.,\n",
       "         83., 128., 102., 302., 198.,  95.,  53., 134., 144., 232.,  81.,\n",
       "        104.,  59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n",
       "        173., 180.,  84., 121., 161.,  99., 109., 115., 268., 274., 158.,\n",
       "        107.,  83., 103., 272.,  85., 280., 336., 281., 118., 317., 235.,\n",
       "         60., 174., 259., 178., 128.,  96., 126., 288.,  88., 292.,  71.,\n",
       "        197., 186.,  25.,  84.,  96., 195.,  53., 217., 172., 131., 214.,\n",
       "         59.,  70., 220., 268., 152.,  47.,  74., 295., 101., 151., 127.,\n",
       "        237., 225.,  81., 151., 107.,  64., 138., 185., 265., 101., 137.,\n",
       "        143., 141.,  79., 292., 178.,  91., 116.,  86., 122.,  72., 129.,\n",
       "        142.,  90., 158.,  39., 196., 222., 277.,  99., 196., 202., 155.,\n",
       "         77., 191.,  70.,  73.,  49.,  65., 263., 248., 296., 214., 185.,\n",
       "         78.,  93., 252., 150.,  77., 208.,  77., 108., 160.,  53., 220.,\n",
       "        154., 259.,  90., 246., 124.,  67.,  72., 257., 262., 275., 177.,\n",
       "         71.,  47., 187., 125.,  78.,  51., 258., 215., 303., 243.,  91.,\n",
       "        150., 310., 153., 346.,  63.,  89.,  50.,  39., 103., 308., 116.,\n",
       "        145.,  74.,  45., 115., 264.,  87., 202., 127., 182., 241.,  66.,\n",
       "         94., 283.,  64., 102., 200., 265.,  94., 230., 181., 156., 233.,\n",
       "         60., 219.,  80.,  68., 332., 248.,  84., 200.,  55.,  85.,  89.,\n",
       "         31., 129.,  83., 275.,  65., 198., 236., 253., 124.,  44., 172.,\n",
       "        114., 142., 109., 180., 144., 163., 147.,  97., 220., 190., 109.,\n",
       "        191., 122., 230., 242., 248., 249., 192., 131., 237.,  78., 135.,\n",
       "        244., 199., 270., 164.,  72.,  96., 306.,  91., 214.,  95., 216.,\n",
       "        263., 178., 113., 200., 139., 139.,  88., 148.,  88., 243.,  71.,\n",
       "         77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n",
       "         58., 262., 206., 233., 242., 123., 167.,  63., 197.,  71., 168.,\n",
       "        140., 217., 121., 235., 245.,  40.,  52., 104., 132.,  88.,  69.,\n",
       "        219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n",
       "         43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
       "        140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
       "         84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
       "         94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
       "        220.,  57.]),\n",
       " 'frame': None,\n",
       " 'DESCR': '.. _diabetes_dataset:\\n\\nDiabetes dataset\\n----------------\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative measure of disease progression one year after baseline.\\n\\n**Data Set Characteristics:**\\n\\n  :Number of Instances: 442\\n\\n  :Number of Attributes: First 10 columns are numeric predictive values\\n\\n  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\\n\\n  :Attribute Information:\\n      - age     age in years\\n      - sex\\n      - bmi     body mass index\\n      - bp      average blood pressure\\n      - s1      tc, T-Cells (a type of white blood cells)\\n      - s2      ldl, low-density lipoproteins\\n      - s3      hdl, high-density lipoproteins\\n      - s4      tch, thyroid stimulating hormone\\n      - s5      ltg, lamotrigine\\n      - s6      glu, blood sugar level\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttps://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\\n(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)',\n",
       " 'feature_names': ['age',\n",
       "  'sex',\n",
       "  'bmi',\n",
       "  'bp',\n",
       "  's1',\n",
       "  's2',\n",
       "  's3',\n",
       "  's4',\n",
       "  's5',\n",
       "  's6'],\n",
       " 'data_filename': 'C:\\\\Users\\\\win10\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\diabetes_data.csv.gz',\n",
       " 'target_filename': 'C:\\\\Users\\\\win10\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\diabetes_target.csv.gz'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "         s4        s5        s6  \n",
       "0 -0.002592  0.019908 -0.017646  \n",
       "1 -0.039493 -0.068330 -0.092204  \n",
       "2 -0.002592  0.002864 -0.025930  \n",
       "3  0.034309  0.022692 -0.009362  \n",
       "4 -0.002592 -0.031991 -0.046641  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_diabetes=pd.DataFrame(dataset.data,columns=['age',\n",
    "  'sex',\n",
    "  'bmi',\n",
    "  'bp',\n",
    "  's1',\n",
    "  's2',\n",
    "  's3',\n",
    "  's4',\n",
    "  's5',\n",
    "  's6'])\n",
    "df_diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Independent and dependent features\n",
    "X=df_diabetes\n",
    "y=dataset['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
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       "      <th>s6</th>\n",
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       "      <th>249</th>\n",
       "      <td>-0.012780</td>\n",
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       "      <td>0.060618</td>\n",
       "      <td>0.052858</td>\n",
       "      <td>0.047965</td>\n",
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       "      <td>0.070211</td>\n",
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       "      <td>-0.041180</td>\n",
       "      <td>-0.088062</td>\n",
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       "      <th>195</th>\n",
       "      <td>0.027178</td>\n",
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       "      <td>0.025051</td>\n",
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       "      <td>0.048477</td>\n",
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       "      <td>0.007837</td>\n",
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       "      <th>69</th>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "          age       sex       bmi        bp        s1        s2        s3  \\\n",
       "249 -0.012780 -0.044642  0.060618  0.052858  0.047965  0.029375 -0.017629   \n",
       "389 -0.005515  0.050680  0.001339 -0.084857 -0.011201 -0.016658  0.048640   \n",
       "121  0.063504 -0.044642  0.017506  0.021872  0.008063  0.021546 -0.036038   \n",
       "195  0.027178  0.050680  0.025051  0.014987  0.025950  0.048477 -0.039719   \n",
       "69   0.016281 -0.044642 -0.046085 -0.005671 -0.075870 -0.061438 -0.013948   \n",
       "\n",
       "           s4        s5        s6  \n",
       "249  0.034309  0.070211  0.007207  \n",
       "389 -0.039493 -0.041180 -0.088062  \n",
       "121  0.034309  0.019908  0.011349  \n",
       "195  0.034309  0.007837  0.023775  \n",
       "69  -0.039493 -0.051401  0.019633  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.198359</td>\n",
       "      <td>0.394904</td>\n",
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       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0.197408</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.221920</td>\n",
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       "      <td>0.180315</td>\n",
       "      <td>-0.379464</td>\n",
       "      <td>0.324882</td>\n",
       "      <td>0.150957</td>\n",
       "      <td>0.193322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>0.198359</td>\n",
       "      <td>0.108261</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.356327</td>\n",
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       "      <td>0.435507</td>\n",
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       "    <tr>\n",
       "      <th>bp</th>\n",
       "      <td>0.394904</td>\n",
       "      <td>0.221920</td>\n",
       "      <td>0.356327</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238303</td>\n",
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       "      <td>-0.192504</td>\n",
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       "      <td>0.420236</td>\n",
       "      <td>0.368881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s1</th>\n",
       "      <td>0.238375</td>\n",
       "      <td>0.057674</td>\n",
       "      <td>0.226047</td>\n",
       "      <td>0.238303</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872781</td>\n",
       "      <td>0.006313</td>\n",
       "      <td>0.559626</td>\n",
       "      <td>0.566311</td>\n",
       "      <td>0.356280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s2</th>\n",
       "      <td>0.192920</td>\n",
       "      <td>0.180315</td>\n",
       "      <td>0.248146</td>\n",
       "      <td>0.165642</td>\n",
       "      <td>0.872781</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.265114</td>\n",
       "      <td>0.684840</td>\n",
       "      <td>0.325756</td>\n",
       "      <td>0.315890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s3</th>\n",
       "      <td>-0.128403</td>\n",
       "      <td>-0.379464</td>\n",
       "      <td>-0.395021</td>\n",
       "      <td>-0.192504</td>\n",
       "      <td>0.006313</td>\n",
       "      <td>-0.265114</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.754616</td>\n",
       "      <td>-0.370521</td>\n",
       "      <td>-0.297055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s4</th>\n",
       "      <td>0.227110</td>\n",
       "      <td>0.324882</td>\n",
       "      <td>0.412826</td>\n",
       "      <td>0.249146</td>\n",
       "      <td>0.559626</td>\n",
       "      <td>0.684840</td>\n",
       "      <td>-0.754616</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.609980</td>\n",
       "      <td>0.436311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s5</th>\n",
       "      <td>0.314153</td>\n",
       "      <td>0.150957</td>\n",
       "      <td>0.435507</td>\n",
       "      <td>0.420236</td>\n",
       "      <td>0.566311</td>\n",
       "      <td>0.325756</td>\n",
       "      <td>-0.370521</td>\n",
       "      <td>0.609980</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.490970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s6</th>\n",
       "      <td>0.351570</td>\n",
       "      <td>0.193322</td>\n",
       "      <td>0.358273</td>\n",
       "      <td>0.368881</td>\n",
       "      <td>0.356280</td>\n",
       "      <td>0.315890</td>\n",
       "      <td>-0.297055</td>\n",
       "      <td>0.436311</td>\n",
       "      <td>0.490970</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "          age       sex       bmi        bp        s1        s2        s3  \\\n",
       "age  1.000000  0.197408  0.198359  0.394904  0.238375  0.192920 -0.128403   \n",
       "sex  0.197408  1.000000  0.108261  0.221920  0.057674  0.180315 -0.379464   \n",
       "bmi  0.198359  0.108261  1.000000  0.356327  0.226047  0.248146 -0.395021   \n",
       "bp   0.394904  0.221920  0.356327  1.000000  0.238303  0.165642 -0.192504   \n",
       "s1   0.238375  0.057674  0.226047  0.238303  1.000000  0.872781  0.006313   \n",
       "s2   0.192920  0.180315  0.248146  0.165642  0.872781  1.000000 -0.265114   \n",
       "s3  -0.128403 -0.379464 -0.395021 -0.192504  0.006313 -0.265114  1.000000   \n",
       "s4   0.227110  0.324882  0.412826  0.249146  0.559626  0.684840 -0.754616   \n",
       "s5   0.314153  0.150957  0.435507  0.420236  0.566311  0.325756 -0.370521   \n",
       "s6   0.351570  0.193322  0.358273  0.368881  0.356280  0.315890 -0.297055   \n",
       "\n",
       "           s4        s5        s6  \n",
       "age  0.227110  0.314153  0.351570  \n",
       "sex  0.324882  0.150957  0.193322  \n",
       "bmi  0.412826  0.435507  0.358273  \n",
       "bp   0.249146  0.420236  0.368881  \n",
       "s1   0.559626  0.566311  0.356280  \n",
       "s2   0.684840  0.325756  0.315890  \n",
       "s3  -0.754616 -0.370521 -0.297055  \n",
       "s4   1.000000  0.609980  0.436311  \n",
       "s5   0.609980  1.000000  0.490970  \n",
       "s6   0.436311  0.490970  1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## correlation\n",
    "X_train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(X_train.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor=DecisionTreeRegressor()\n",
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "param={\n",
    "    'criterion':['squared_error','friedman_mse','absolute_error'],\n",
    "    'splitter':['best','random'],\n",
    "    'max_depth':[1,2,3,4,5,10,15,20,25],\n",
    "    'max_features':['auto','sqrt','log2']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': ['squared_error', 'friedman_mse', 'absolute_error'],\n",
       " 'splitter': ['best', 'random'],\n",
       " 'max_depth': [1, 2, 3, 4, 5, 10, 15, 20, 25],\n",
       " 'max_features': ['auto', 'sqrt', 'log2']}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor=DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid=GridSearchCV(regressor,param_grid=param,cv=5,scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeRegressor(),\n",
       "             param_grid={'criterion': ['squared_error', 'friedman_mse',\n",
       "                                       'absolute_error'],\n",
       "                         'max_depth': [1, 2, 3, 4, 5, 10, 15, 20, 25],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'splitter': ['best', 'random']},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'friedman_mse',\n",
       " 'max_depth': 4,\n",
       " 'max_features': 'log2',\n",
       " 'splitter': 'random'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## predictions\n",
    "y_pred=grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.20367583041910708\n",
      "59.4482544111221\n",
      "5038.877430131429\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(y_test,y_pred))\n",
    "print(mean_absolute_error(y_test,y_pred))\n",
    "print(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "selectedmodel=DecisionTreeRegressor(criterion='friedman_mse',max_depth=4,max_features='log2',splitter='random')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='friedman_mse', max_depth=4,\n",
       "                      max_features='log2', splitter='random')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selectedmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(470.8125, 489.24, 'X[7] <= 0.041\\nfriedman_mse = 5757.456\\nsamples = 309\\nvalue = 152.738'),\n",
       " Text(279.0, 380.52000000000004, 'X[3] <= -0.046\\nfriedman_mse = 5400.103\\nsamples = 262\\nvalue = 143.752'),\n",
       " Text(139.5, 271.8, 'X[3] <= -0.082\\nfriedman_mse = 3335.255\\nsamples = 47\\nvalue = 108.979'),\n",
       " Text(69.75, 163.08000000000004, 'X[9] <= -0.007\\nfriedman_mse = 442.438\\nsamples = 8\\nvalue = 80.25'),\n",
       " Text(34.875, 54.360000000000014, 'friedman_mse = 221.139\\nsamples = 6\\nvalue = 70.833'),\n",
       " Text(104.625, 54.360000000000014, 'friedman_mse = 42.25\\nsamples = 2\\nvalue = 108.5'),\n",
       " Text(209.25, 163.08000000000004, 'X[2] <= 0.099\\nfriedman_mse = 3724.625\\nsamples = 39\\nvalue = 114.872'),\n",
       " Text(174.375, 54.360000000000014, 'friedman_mse = 2379.85\\nsamples = 38\\nvalue = 108.789'),\n",
       " Text(244.125, 54.360000000000014, 'friedman_mse = 0.0\\nsamples = 1\\nvalue = 346.0'),\n",
       " Text(418.5, 271.8, 'X[6] <= -0.069\\nfriedman_mse = 5529.373\\nsamples = 215\\nvalue = 151.353'),\n",
       " Text(348.75, 163.08000000000004, 'X[5] <= -0.026\\nfriedman_mse = 6183.84\\nsamples = 5\\nvalue = 175.4'),\n",
       " Text(313.875, 54.360000000000014, 'friedman_mse = 4704.0\\nsamples = 4\\nvalue = 200.0'),\n",
       " Text(383.625, 54.360000000000014, 'friedman_mse = 0.0\\nsamples = 1\\nvalue = 77.0'),\n",
       " Text(488.25, 163.08000000000004, 'X[3] <= 0.029\\nfriedman_mse = 5499.695\\nsamples = 210\\nvalue = 150.781'),\n",
       " Text(453.375, 54.360000000000014, 'friedman_mse = 4462.947\\nsamples = 149\\nvalue = 135.785'),\n",
       " Text(523.125, 54.360000000000014, 'friedman_mse = 6141.127\\nsamples = 61\\nvalue = 187.41'),\n",
       " Text(662.625, 380.52000000000004, 'X[6] <= -0.081\\nfriedman_mse = 4790.184\\nsamples = 47\\nvalue = 202.83'),\n",
       " Text(627.75, 271.8, 'friedman_mse = 0.0\\nsamples = 1\\nvalue = 341.0'),\n",
       " Text(697.5, 271.8, 'X[3] <= 0.026\\nfriedman_mse = 4470.274\\nsamples = 46\\nvalue = 199.826'),\n",
       " Text(627.75, 163.08000000000004, 'X[2] <= 0.077\\nfriedman_mse = 4961.358\\nsamples = 27\\nvalue = 183.778'),\n",
       " Text(592.875, 54.360000000000014, 'friedman_mse = 4808.748\\nsamples = 24\\nvalue = 176.792'),\n",
       " Text(662.625, 54.360000000000014, 'friedman_mse = 2668.222\\nsamples = 3\\nvalue = 239.667'),\n",
       " Text(767.25, 163.08000000000004, 'X[6] <= -0.035\\nfriedman_mse = 2886.338\\nsamples = 19\\nvalue = 222.632'),\n",
       " Text(732.375, 54.360000000000014, 'friedman_mse = 2280.996\\nsamples = 15\\nvalue = 230.733'),\n",
       " Text(802.125, 54.360000000000014, 'friedman_mse = 3987.188\\nsamples = 4\\nvalue = 192.25')]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Visualize the Decision Tree\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import tree\n",
    "plt.figure(figsize=(15,10))\n",
    "tree.plot_tree(selectedmodel,filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
